A MFD (Multi-Function Device) is a rendering device or office machine, which incorporates the functionality of multiple devices in one apparatus or system, so as to have a smaller footprint in a home or small business setting, or to provide centralized document management/distribution/production in the context of, for example, a large-office setting. A typical MFP may provide a combination of some or all of the following capabilities: printer, scanner, photocopier, fax machine, e-mail capability, and so forth. Networked MFDs (Multi-Function Devices) generally interact with an assemblage of varying rendering devices, client computers, servers, and other components that are connected to and communicate over a network.
Diagnostic techniques are generally employed to fix problems in more complex MFDs, and to identify the cause of failure in a machine component from a failure symptom, as well as to predict the occurrence of a particular failure type from pre-failure data. The problems that can be encountered with a fleet of MFDs before an MFD product is launched are often easily observable, repeatable, and diagnosable by an engineering team. Such problems can be repaired utilizing built-in diagnosis tools such as, for example, fault codes, electronic documents, and knowledge base documentation provided in association with the MFDs.
Problems with indirect causes are more difficult to diagnose and repair; however, knowledge of such problems can be gained by a CSE (Customer Service Engineer) who exchanges information anecdotally, or through e-mail, forums, or other general purpose modes of communication. Furthermore, different types of problems may occur in different operating conditions or phases associated with each MFD in an MFD fleet.
Conventional state tracking systems for diagnosing actual, pending, difficult or previously unseen problems involve the provision of data snapshots associated with the MFDs with respect to a relatively infrequent cadence to a back end server, where data are later processed. The relative infrequency of the data may lead to a so-called “Nyquist problem,” wherein the state of a device may change many times between data snapshots so that the data do not capture a useful time course for data stream mining. One approach involves acquiring and sending data to the back office systems more frequently or alternatively to store large volumes of data locally and send it in a big data package. Transmitting and processing such enormous amounts of data is bandwidth and computationally intensive, time consuming, and non-specific to the immediate usage profile of the MFDs. Additionally, determining the root causes for such difficult problems is an extremely challenging task.
Based on the foregoing, it is believed that a need exists for an improved distributed data mining system and method for determining the root cause of problems associated with members of an MFD cloud, as described in greater detail herein. In addition, a need exists for solutions involving situations in which a particular machine member of an MFP cloud experiences a particular problem. The cloud itself is robust because of the distributed nature of the cloud. That is, a large fraction of the cloud may fail but the remaining, functional members can sustain the cloud even in the absence of many of its members.